class_docstr: Convert graph shape from Channel first (NCHW) to Channel last (NHWC) format.
  
  Supported functions: `Convolution`, `Deconvolution`, `BatchNormalization`,
  `MaxPooling`, `AveragePooling`, `SumPooling`, `Unpooling`, `Concatenate`
  
  Args:
      inputs (list of nn.Variable): Original very beginning inputs (NCHW) of a network.
      inputs_cl (list of nn.Variable): Channel last version of very beginning inputs (NHWC) of a network.
        If this is not given, `inputs_cl` are generated internally and holded.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.ChannelLastModifier(<inputs of pred>)]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
code: def modify(self, f, inputs):
      fname = f.info.type_name
      args = f.info.args
      if fname in [
          "Convolution",
          "Deconvolution",
          "BatchNormalization",
          "MaxPooling",
          "AveragePooling",
          "SumPooling",
          "Unpooling",
          "Concatenate",
          "Affine",
      ]:
          o = self.connect(fname, inputs, args)
          return o
  
file_path: nnabla/python/src/nnabla/experimental/graph_converters/channel_last.py
imports: import nnabla
  import nnabla.functions
  import numpy
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 4 for calling the ChannelLastModifier obj's underlying member method code with example usage: obj.modify(f, inputs) and returns: o